There are many challenges in developing fully automatic analysis systems for medical images: sensor noise and artifacts, anatomic variability, differences between acquisition protocols. To get robust results, each of these sets of challenges may be addressed in a systematic and principled way.
It has long been recognized that the presence of the pectoral muscle can substantially interfere with automated analysis algorithms and visualization of the breast. For example, algorithms for detection of mass lesions, estimation of breast density, or the registration of a current examination with a prior examination can all be significantly influenced by the presence of the pectoral muscle. Therefore, it is not surprising to find a large amount of literature on the problem of segmenting the pectoral muscle. The results from a pectoral muscle segmentation algorithm can be used explicitly and/or implicitly. Explicit use means that pixels within a segmentation mask may simply be ignored or treated separately (e.g., dense tissue segmentation). Implicit use means that the segmentation result may be used to suppress (equalize) the gray scale bias in the pectoral region. Implicit use, while highly desirable, is a challenge, as imperfect segmentation can lead to suppression artifacts.